######## snakemake preamble start (automatically inserted, do not edit) ########
import sys; sys.path.extend(['/home/oma21/miniconda3/envs/enm_snakemake/lib/python3.9/site-packages', '/home/oma21/enm_package/notebooks']); import pickle; snakemake = pickle.loads(b'\x80\x04\x95\x99\x04\x00\x00\x00\x00\x00\x00\x8c\x10snakemake.script\x94\x8c\tSnakemake\x94\x93\x94)\x81\x94}\x94(\x8c\x05input\x94\x8c\x0csnakemake.io\x94\x8c\nInputFiles\x94\x93\x94)\x81\x94(\x8c\x17data/interim/pcc.pickle\x94\x8c\x1bdata/interim/sensors_df.csv\x94\x8c\x1ddata/interim/effectors_df.csv\x94e}\x94(\x8c\x06_names\x94}\x94(\x8c\x10pickle_file_name\x94K\x00N\x86\x94\x8c\x0bsensors_pcc\x94K\x01N\x86\x94\x8c\x0ceffector_pcc\x94K\x02N\x86\x94u\x8c\x12_allowed_overrides\x94]\x94(\x8c\x05index\x94\x8c\x04sort\x94eh\x18\x8c\tfunctools\x94\x8c\x07partial\x94\x93\x94h\x06\x8c\x19Namedlist._used_attribute\x94\x93\x94\x85\x94R\x94(h\x1e)}\x94\x8c\x05_name\x94h\x18sNt\x94bh\x19h\x1ch\x1e\x85\x94R\x94(h\x1e)}\x94h"h\x19sNt\x94bh\x10h\nh\x12h\x0bh\x14h\x0cub\x8c\x06output\x94h\x06\x8c\x0bOutputFiles\x94\x93\x94)\x81\x94\x8c)reports/01-Fig1bcd_3c_4b_5df-052421.ipynb\x94a}\x94(h\x0e}\x94\x8c\x08notebook\x94K\x00N\x86\x94sh\x16]\x94(h\x18h\x19eh\x18h\x1ch\x1e\x85\x94R\x94(h\x1e)}\x94h"h\x18sNt\x94bh\x19h\x1ch\x1e\x85\x94R\x94(h\x1e)}\x94h"h\x19sNt\x94bh/h,ub\x8c\x06params\x94h\x06\x8c\x06Params\x94\x93\x94)\x81\x94}\x94(h\x0e}\x94h\x16]\x94(h\x18h\x19eh\x18h\x1ch\x1e\x85\x94R\x94(h\x1e)}\x94h"h\x18sNt\x94bh\x19h\x1ch\x1e\x85\x94R\x94(h\x1e)}\x94h"h\x19sNt\x94bub\x8c\twildcards\x94h\x06\x8c\tWildcards\x94\x93\x94)\x81\x94}\x94(h\x0e}\x94h\x16]\x94(h\x18h\x19eh\x18h\x1ch\x1e\x85\x94R\x94(h\x1e)}\x94h"h\x18sNt\x94bh\x19h\x1ch\x1e\x85\x94R\x94(h\x1e)}\x94h"h\x19sNt\x94bub\x8c\x07threads\x94K\x01\x8c\tresources\x94h\x06\x8c\tResources\x94\x93\x94)\x81\x94(K\x01K\x01e}\x94(h\x0e}\x94(\x8c\x06_cores\x94K\x00N\x86\x94\x8c\x06_nodes\x94K\x01N\x86\x94uh\x16]\x94(h\x18h\x19eh\x18h\x1ch\x1e\x85\x94R\x94(h\x1e)}\x94h"h\x18sNt\x94bh\x19h\x1ch\x1e\x85\x94R\x94(h\x1e)}\x94h"h\x19sNt\x94bh_K\x01haK\x01ub\x8c\x03log\x94h\x06\x8c\x03Log\x94\x93\x94)\x81\x94\x8c)reports/01-Fig1bcd_3c_4b_5df-052421.ipynb\x94a}\x94(h\x0e}\x94h/K\x00N\x86\x94sh\x16]\x94(h\x18h\x19eh\x18h\x1ch\x1e\x85\x94R\x94(h\x1e)}\x94h"h\x18sNt\x94bh\x19h\x1ch\x1e\x85\x94R\x94(h\x1e)}\x94h"h\x19sNt\x94bh/hpub\x8c\x06config\x94}\x94\x8c\x04rule\x94\x8c\x0ffigure_networks\x94\x8c\x0fbench_iteration\x94N\x8c\tscriptdir\x94\x8c!/home/oma21/enm_package/notebooks\x94ub.'); from snakemake.logging import logger; logger.printshellcmds = False; import os; os.chdir('/home/oma21/enm_package');
######## snakemake preamble end #########

Table of Contents

• 1  Import packages
• 2  Read Enm pickle object
• 3  Calculate node positions with spring layout
• 4  Network plot
  • 4.1  Figure 1B, left
  • 4.2  Figure 1B, right
• 5  Figure 1C
• 6  Figure 1D
• 7  Figure 3C
• 8  Figure 4B
• 9  Figure 5D/F

Import packages

%load_ext autoreload
%autoreload 2
from matplotlib.lines import Line2D
import qgrid
import matplotlib.pyplot as plt
import matplotlib as mpl
import networkx as nx
import numpy as np
import pandas as pd
import pickle
import os
import re
import itertools as itr
from enm.visualize import plot_correlation_density, plot_vector, plot_lambda_collectivity
from enm.Enm import Enm
from enm.utils import *

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

#os.chdir('../')

Read Enm pickle object

with open(snakemake.input.pickle_file_name,'rb') as f:
    e_pcc = pickle.load(f)

Calculate node positions with spring layout

This could be different from the ones shown in paper due to random node position calculation

This does not change any claims in the paper

e_pcc.spring_pos(seed=12)

pos =e_pcc.graph_gc.nodes('pos')# nx.spring_layout(e_pcc.graph_gc,k=0.6,iterations=200, seed=11)#

Network plot

Figure 1B, left

fig, ax =plt.subplots()

nx.draw_networkx_nodes(e_pcc.graph_gc,
                           node_size=0.2,
                           alpha=0.5,
                           node_color='k',
                           pos=pos,
                         ax=ax
                           # node_shape=matplotlib.markers.MarkerStyle(marker='o',fillstyle='full')
                           )
nx.draw_networkx_edges(e_pcc.graph_gc,
                           alpha= 0.1,
                           width= 0.1,
                           edge_color='k',
                           pos=pos,
                           label='PCC>0.2',ax=ax)

<matplotlib.collections.LineCollection at 0x7faa5ef77c90>

Figure 1B, right

e_pcc.heatmap_annotated()

/home/oma21/enm_package/enm/visualize.py:334: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax_heatmap.set_xticklabels(idx1_cl, minor=False)
/home/oma21/enm_package/enm/visualize.py:342: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax_heatmap.set_yticklabels(idx2_cl, minor=False)

(array([0.00225456, 0.00212964, 0.00064455, ..., 0.0018722 , 0.00188671,
        0.00196717]),
 array([0.00670243, 0.00670243, 0.00610539, ..., 0.00030683, 0.00022774,
        0.00013241]))

Figure 1C

get_clustered_nodes finds the gene names belonging the outmost, smaller cluster for rows and columns as shown.

cc = e_pcc.df.iloc[e_pcc.get_clustered_nodes('column'),:]
rc = e_pcc.df.iloc[e_pcc.get_clustered_nodes('row'),:]

rc: row cluster

cc: column cluster

df = e_pcc.df
df['cc'] = ['cc' if i in cc.orf_name.tolist() else 'other' for i in df.orf_name.tolist()]
df['rc'] = ['rc' if i in rc.orf_name.tolist() else 'other' for i in df.orf_name.tolist()]

import seaborn as sns
color = {'cc': 'orange','other':'k','rc':'orangered'}
fig, ax = plt.subplots(1,2,figsize=(8,4))

sns.boxplot(data=df, x='rc',y='eff',order=['rc','other'],palette=color,ax=ax[0])
ax[0].set_xticklabels(['Distinct\nRow\nCluster', 'Other genes'])
ax[0].set_xlabel('Row clusters')
ax[0].set_ylabel('Effectiveness')

sns.boxplot(data=df, x='cc',y='sens',order=['cc','other'],palette=color,ax=ax[1])
ax[1].set_xticklabels(['Distinct\nColumn\nCluster', 'Other genes'])
ax[1].set_xlabel('Columns clusters')
ax[1].set_ylabel('Sensitivity')

plt.tight_layout()

#plt.savefig('reports/figures/network_images_040221/colcluster_sens.png')

Figure 1D

fig, ax = plt.subplots(figsize=(12,12))
#axs = ax.ravel()
legend_elements = [    ]

#for i in range(len(sensor_order)):
nx.draw_networkx_nodes(e_pcc.graph_gc, ax =ax , pos=pos,node_size=1, node_color='black')
nx.draw_networkx_edges(e_pcc.graph_gc, ax =ax , pos=pos,node_size=1, edge_color='black',alpha=0.2, width=0.1)
nx.draw_networkx_nodes(e_pcc.graph_gc, 
                       nodelist=rc.orf_name.tolist(),
                       ax=ax, 
                       pos=pos,
                       node_color='orangered',
                      edgecolors='black',
                      node_size=100,
                      node_shape='d')

ax.set_facecolor('white')
legend_elements.extend(
    [Line2D([0], [0], marker='d', color='black', label='Row cluster\ngenes',
                              markerfacecolor='orangered', markersize=50, linestyle="None"),
     Line2D([0], [0], marker='o', color='black', label='Other Genes',
                              markerfacecolor='black', markersize=25, linestyle="None"),
#                    Line2D([0], [0], marker='o', color='black', label='Effectors',
#                               markerfacecolor='black', markersize=10, linestyle="None"),
#                    Line2D([0], [0], marker='^', color='black', label='Sensors',
#                               markerfacecolor='black', markersize=10, linestyle="None"),
                       Line2D([0], [0], marker='o', color='black', label= 'High\nfunctional\nsimilarity',
                              markerfacecolor='black', markersize=0, linestyle="-", alpha=0.5,lw=10)
    ]
)
lgd = ax.legend(handles=legend_elements, fontsize=40,loc='center left', bbox_to_anchor=(1.1, 0.5))


#nx.draw_networkx_edges(nx.induced_subgraph(e_pcc.graph_gc, effector_pcc.orf_name.tolist()), ax=ax , pos=pos, edge_color='blue',alpha=0.5)
#plt.savefig('reports/figures/network_images_040221/rowcluster_nw_map.png',bbox_inches='tight')

fig, ax = plt.subplots(figsize=(12,12))
#axs = ax.ravel()
legend_elements = [    ]

#for i in range(len(sensor_order)):
nx.draw_networkx_nodes(e_pcc.graph_gc, ax =ax , pos=pos,node_size=1, node_color='black')
nx.draw_networkx_edges(e_pcc.graph_gc, ax =ax , pos=pos,node_size=1, edge_color='black',alpha=0.2, width=0.1)
nx.draw_networkx_nodes(e_pcc.graph_gc, 
                       nodelist=cc.orf_name.tolist(),
                       ax=ax, 
                       pos=pos,
                       node_color='orange',
                      edgecolors='black',
                      node_size=100,
                      node_shape='D')

ax.set_facecolor('white')
legend_elements.extend([
    Line2D([0], [0], marker='D', color='black', label='Column cluster\ngenes',
                              markerfacecolor='orange', markersize=50, linestyle="None"),
    Line2D([0], [0], marker='o', color='black', label='Other Genes',
                              markerfacecolor='black', markersize=25, linestyle="None"),
                       Line2D([0], [0], marker='o', color='black', label= 'High\nfunctional\nsimilarity',
                              markerfacecolor='black', markersize=0, linestyle="-", alpha=0.5,lw=10),
    ]
)
lgd = ax.legend(handles=legend_elements, fontsize=40,loc='center left', bbox_to_anchor=(1.1, 0.5))


#nx.draw_networkx_edges(nx.induced_subgraph(e_pcc.graph_gc, effector_pcc.orf_name.tolist()), ax=ax , pos=pos, edge_color='blue',alpha=0.5)
#plt.savefig('reports/figures/network_images_040221/colcluster_nw_map.png',bbox_inches='tight')

Figure 3C

sensor_go_rename = {
  "cellular response to iron ion starvation":'Iron ion transport' ,
"mitochondria-nucleus signaling pathway":  "Mitochondria-nucleus\nsignaling pathway\nand\nTricarboxylic acid cycle",
"phenylalanine transport":  "Phenylalanine transport",
"hexose metabolic process":  "Hexose metabolic process",
"tricarboxylic acid cycle":  "Tricarboxylic acid cycle"
}

sensors_pcc = pd.read_csv(snakemake.input.sensors_pcc)
sensor_colors = ["#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#FFFF33"]
sensors_pcc['go_group']=sensors_pcc['go_group'].map(sensor_go_rename,na_action='ignore')
sensor_order = sensors_pcc.groupby('go_group').sens.median().sort_values().index.tolist()

fig, ax = plt.subplots(figsize=(12,12))
#axs = ax.ravel()
legend_elements = [    ]

#for i in range(len(sensor_order)):
e_pcc.plot_network_spring(ax=ax,
                          node_size=1,
                          node_color='black',
 #                        node_size = [100 if i in sensors_pcc.orf_name.values or i in effector_pcc.orf_name.values else 1 for i in e_pcc.nodes],
                         #node_color = ['red' if i in sensors_pcc.orf_name.values else 'blue' if i in effector_pcc.orf_name.values else 'black' for i in e_pcc.nodes],
                         edge_color='black',savefig=False)
    #                         node_shape=['^' if i in sensors_pcc.orf_name.values else 'v' if i in effector_pcc.orf_name.values else 'o' for i in e_pcc.nodes])
    # nx.draw_networkx_nodes(e_pcc.graph_gc, nodelist=sensors_pcc.orf_name.values, node_size=200, pos=pos,
    #                           node_color='black',
    #                           node_shape='^',edgecolors='black',
    #                           linewidths=1)
nx.draw_networkx_nodes(nx.induced_subgraph(e_pcc.graph_gc, sensors_pcc.orf_name.tolist()),
                       pos=pos, 
                       node_color='black', alpha=1, node_shape='^')

for itr, i in enumerate(sensor_order):
    #print(i, effector_colors[itr])

    orf_names_to_plot = sensors_pcc.loc[sensors_pcc.go_group==i, 'orf_name'].tolist()
    nx.draw_networkx_nodes(e_pcc.graph_gc, nodelist=orf_names_to_plot, node_size=200, pos=pos,
                          node_color=sensor_colors[itr],
                          node_shape='^',edgecolors='black',
                          linewidths=1)
    legend_elements.append(
        Line2D([0], [0], marker='^', color='black', label=f'Sensors ({i})',
                              markerfacecolor=sensor_colors[itr], markersize=30, linestyle="None")
    )
ax.set_facecolor('white')
legend_elements.append(
        Line2D([0], [0], marker='^', color='black', label=f'Sensors (No GO Enrichments )',
                              markerfacecolor='black', markersize=30, linestyle="None")
    )
legend_elements.extend(
    [Line2D([0], [0], marker='o', color='black', label='Other Genes',
                              markerfacecolor='black', markersize=10, linestyle="None"),
#                    Line2D([0], [0], marker='o', color='black', label='Effectors',
#                               markerfacecolor='black', markersize=10, linestyle="None"),
#                    Line2D([0], [0], marker='^', color='black', label='Sensors',
#                               markerfacecolor='black', markersize=10, linestyle="None"),
                       Line2D([0], [0], marker='o', color='black', label= 'High functional similarity',
                              markerfacecolor='black', markersize=0, linestyle="-", alpha=0.5, lw=10),
                   Line2D([0], [0], marker='o', color='red', label= 'Sensor-Sensor edges',
                              markerfacecolor='#018571', markersize=0, linestyle="-",lw=10)
                   #Line2D([0], [0], marker='o', color='blue', label= 'Effector-Effector edges',
    #                          markerfacecolor='#a6611a', markersize=0, linestyle="-")
    ]
)
#lgd = ax.legend(handles=legend_elements, fontsize=22,loc='center left', bbox_to_anchor=(1.1, 0.5),ncol=5)
nx.draw_networkx_edges(nx.induced_subgraph(e_pcc.graph_gc, sensors_pcc.orf_name.tolist()),pos=pos, edge_color='red', alpha=0.5)

<matplotlib.collections.LineCollection at 0x7fa9f0906250>

fig = plt.figure()
figlegend = plt.figure(figsize=(2,2))
ax = fig.add_subplot(111)
#lines = ax.plot(range(10), plt.randn(10), range(10), plt.randn(10))
ax.axis('off')
lgd = ax.legend(handles=legend_elements, fontsize=40, loc='center',ncol=2)
fig.show()

<Figure size 144x144 with 0 Axes>

Figure 4B

effector_go_group_map = {
    "phosphatidylinositol biosynthetic process":"Golgi vesicle transport",
    "mitochondrial cytochrome c oxidase assembly":"Respiratory complex assembly",
    "replication fork protection":"Chromosome segregation"
}

effector_pcc = pd.read_csv(snakemake.input.effector_pcc)
effector_colors = ["#A65628", "#F781BF", "#999999"]
effector_pcc['go_group'] = effector_pcc['go_group'].map(effector_go_group_map)
effector_order = effector_pcc.groupby('go_group').eff.median().sort_values().index.tolist()

fig, ax = plt.subplots(figsize=(12,12))
#axs = ax.ravel()
legend_elements = [    ]

#for i in range(len(sensor_order)):
e_pcc.plot_network_spring(ax=ax,
                          node_size=1,
                          node_color='black',
 #                        node_size = [100 if i in sensors_pcc.orf_name.values or i in effector_pcc.orf_name.values else 1 for i in e_pcc.nodes],
                         #node_color = ['red' if i in sensors_pcc.orf_name.values else 'blue' if i in effector_pcc.orf_name.values else 'black' for i in e_pcc.nodes],
                         edge_color='black',savefig=False)
    #                         node_shape=['^' if i in sensors_pcc.orf_name.values else 'v' if i in effector_pcc.orf_name.values else 'o' for i in e_pcc.nodes])
    # nx.draw_networkx_nodes(e_pcc.graph_gc, nodelist=sensors_pcc.orf_name.values, node_size=200, pos=pos,
    #                           node_color='black',
    #                           node_shape='^',edgecolors='black',
    #                           linewidths=1)

for itr, i in enumerate(effector_order):
   # print(i, effector_colors[itr])
    #print(itr)
    
    orf_names_to_plot = effector_pcc.loc[effector_pcc.go_group==i, 'orf_name'].tolist()
    nx.draw_networkx_nodes(e_pcc.graph_gc, nodelist=orf_names_to_plot, node_size=200, pos=pos,
                          node_color=effector_colors[itr],
                          node_shape='o',edgecolors='black',
                          linewidths=1)
    legend_elements.append(
        Line2D([0], [0], marker='o', color='black', label=f'Effectors ({i})',
                              markerfacecolor=effector_colors[itr], markersize=30, linestyle="None")
    )
ax.set_facecolor('white')
legend_elements.extend(
    [Line2D([0], [0], marker='o', color='black', label='Other Genes',
                              markerfacecolor='black', markersize=10, linestyle="None"),
#                    Line2D([0], [0], marker='o', color='black', label='Effectors',
#                               markerfacecolor='black', markersize=10, linestyle="None"),
#                    Line2D([0], [0], marker='^', color='black', label='Sensors',
#                               markerfacecolor='black', markersize=10, linestyle="None"),
                       Line2D([0], [0], marker='o', color='black', label= 'High functional similarity',
                              markerfacecolor='black', markersize=0, linestyle="-", alpha=0.5,lw=10),
                   Line2D([0], [0], marker='o', color='blue', label= 'Effector-Effector edges',
                             markerfacecolor='#a6611a', markersize=0, linestyle="-",lw=10)
    ]
)
lgd = ax.legend(handles=legend_elements, fontsize=22,loc='center left', bbox_to_anchor=(1.1, 0.5))


nx.draw_networkx_edges(nx.induced_subgraph(e_pcc.graph_gc, effector_pcc.orf_name.tolist()), ax=ax , pos=pos, edge_color='blue',alpha=0.5)
#plt.savefig('reports/figures/network_images_040221/tmp_nw_eff.png',bbox_inches='tight')

<matplotlib.collections.LineCollection at 0x7fa9ed9afb10>

Figure 5D/F

#define source and target effector/sensor clusters

eff_group = 'Chromosome segregation'
sens_group = "Mitochondria-nucleus\nsignaling pathway\nand\nTricarboxylic acid cycle"
#select source and target gene from respective clusters
source = 'ctf4'
target = 'rtg1'
#calculate source and tartget
l1 = e_pcc.get_prs_weighted_path(source,target)[1]
sub = nx.induced_subgraph(e_pcc.graph_gc, l1)

fig, ax = plt.subplots(figsize=(12,12))
legend_elements = [    ]
nx.draw_networkx_nodes(e_pcc.graph_gc, pos=pos, node_size=1, ax=ax, node_color='black')

nx.draw_networkx_nodes(sub,pos=pos,alpha=0.8,
                    #  node_size = [prs_mat_df.loc[source,:].to_dict()[i]*10000 for i in sub.nodes],
       node_color = ['red'  if i ==target or i == 'fum1' or i=='fbp1' else 'blue' if i==source  else 'black' for i in sub.nodes])

nx.draw_networkx_edges(sub,pos=pos)

for itr, i in enumerate(sensor_order):
    if i ==sens_group:
        orf_names_to_plot = sensors_pcc.loc[sensors_pcc.go_group==i, 'orf_name'].tolist()
        nx.draw_networkx_nodes(e_pcc.graph_gc, nodelist=orf_names_to_plot, node_size=200, pos=pos,
                              node_color=sensor_colors[itr],
                              node_shape='^',edgecolors='black',
                              linewidths=1)
        nx.draw_networkx_edges(nx.induced_subgraph(e_pcc.graph_gc, orf_names_to_plot), ax=ax , pos=pos, edge_color='red',alpha=0.5)

        legend_elements.append(
            Line2D([0], [0], marker='^', color='black', label=f'Sensors ({i})',
                                  markerfacecolor=sensor_colors[itr], markersize=12, linestyle="None")
        )

for itr, i in enumerate(effector_order):
    if i == eff_group:
        orf_names_to_plot = effector_pcc.loc[effector_pcc.go_group==i,'orf_name'].tolist()
        nx.draw_networkx_nodes(e_pcc.graph_gc, nodelist=orf_names_to_plot, node_size=200, pos=pos,
                              node_color=effector_colors[itr],
                              node_shape='o',edgecolors='black',
                              linewidths=1)
        nx.draw_networkx_edges(nx.induced_subgraph(e_pcc.graph_gc, orf_names_to_plot), ax=ax , pos=pos, edge_color='blue',alpha=0.5)
ax.set_facecolor('white')
#plt.savefig('tmp_nw.png',bbox_inches='tight')

eff_group = 'Respiratory complex assembly'
sens_group = 'Iron ion transport'

source = 'aim10'
target = 'fet3'
l1 = e_pcc.get_prs_weighted_path(source,target)[1]
sub = nx.induced_subgraph(e_pcc.graph_gc, l1)

fig, ax = plt.subplots(figsize=(12,12))
legend_elements = [    ]
nx.draw_networkx_nodes(e_pcc.graph_gc, pos=pos, node_size=1, ax=ax, node_color='black')
nx.draw_networkx_nodes(sub,pos=pos,alpha=0.8,
                    #  node_size = [prs_mat_df.loc[source,:].to_dict()[i]*10000 for i in sub.nodes],
       node_color = ['red'  if i ==target or i == 'fum1' or i=='fbp1' else 'blue' if i==source  else 'black' for i in sub.nodes])

nx.draw_networkx_edges(sub,pos=pos)

for itr, i in enumerate(sensor_order):
    #print(i, effector_colors[itr])
    if i ==sens_group:
        orf_names_to_plot = sensors_pcc.loc[sensors_pcc.go_group==i, 'orf_name'].tolist()
        nx.draw_networkx_nodes(e_pcc.graph_gc, nodelist=orf_names_to_plot, node_size=200, pos=pos,
                              node_color=sensor_colors[itr],
                              node_shape='^',edgecolors='black',
                              linewidths=1)
        nx.draw_networkx_edges(nx.induced_subgraph(e_pcc.graph_gc, orf_names_to_plot), ax=ax , pos=pos, edge_color='red',alpha=0.5)

        legend_elements.append(
            Line2D([0], [0], marker='^', color='black', label=f'Sensors ({i})',
                                  markerfacecolor=sensor_colors[itr], markersize=12, linestyle="None")
        )

for itr, i in enumerate(effector_order):
    if i == eff_group:
        orf_names_to_plot = effector_pcc.loc[effector_pcc.go_group==i,'orf_name'].tolist()
        nx.draw_networkx_nodes(e_pcc.graph_gc, nodelist=orf_names_to_plot, node_size=200, pos=pos,
                              node_color=effector_colors[itr],
                              node_shape='o',edgecolors='black',
                              linewidths=1)
        nx.draw_networkx_edges(nx.induced_subgraph(e_pcc.graph_gc, orf_names_to_plot), ax=ax , pos=pos, edge_color='blue',alpha=0.5)

ax.set_facecolor('white')
#plt.savefig('tmp_nw.png',bbox_inches='tight')